Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) instruments are capable of saving an entire mass spectrum at each pixel of an image, allowing for retrospective analysis of masses that were not selected for analysis during data collection. These TOF-SIMS spectral images contain a wealth of information, but few tools are available to assist the analyst in visualizing the entire raw data set and as a result, most of the data are not analyzed. Automated, nonbiased, multivariate statistical analysis (MVSA) techniques are useful for converting the massive amount of data into a smaller number of chemical components (spectra and images) that are needed to fully describe the TOF-SIMS measurement. Many samples require two back-to-back TOF-SIMS measurements in order to fully characterize the sample, one measurement of the fraction of positively charged secondary ions (positive ion fraction) and one measurement of the fraction of negatively charged secondary ions (negative ion fraction). Each measurement then needs to be individually evaluated. In this paper, we report the first MVSA analysis of a concatenated TOF-SIMS date set comprising positive ion and negative ion spectral images collected on the same region of a sample. MVSA of concatenated data sets provides results that are intuitive and fully describe the sample. The analytical insight provided by MVSA of the concatenated data set was not obtained when either polarity data set was analyzed separately. © 2005 American Chemical Society.

Abstract not provided.

The performance and reliability of microelectromechanical (MEMS) devices can be highly dependent on the control of the surface energetics in these structures. Examples of this sensitivity include the use of surface modifying chemistries to control stiction, to minimize friction and wear, and to preserve favorable electrical characteristics in surface micromachined structures. Silane modification of surfaces is one classic approach to controlling stiction in Si-based devices. The time-dependent efficacy of this modifying treatment has traditionally been evaluated by studying the impact of accelerated aging on device performance and conducting subsequent failure analysis. Our interest has been in identifying aging related chemical signatures that represent the early stages of processes like silane displacement or chemical modification that eventually lead to device performance changes. We employ a series of classic surface characterization techniques along with multivariate statistical methods to study subtle changes in the silanized silicon surface and relate these to degradation mechanisms. Examples include the use of spatially resolved time-of-flight secondary ion mass spectrometric, photoelectron spectroscopic, photoluminescence imaging, and scanning probe microscopic techniques to explore the penetration of water through a silane monolayer, the incorporation of contaminant species into a silane monolayer, and local displacement of silane molecules from the Si surface. We have applied this analytical methodology at the Si coupon level up to MEMS devices. This approach can be generalized to other chemical systems to address issues of new materials integration into micro- and nano-scale systems.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Analytical instrumentation such as time-of-flight secondary ion mass spectrometry (ToF-SIMS) provides a tremendous quantity of data since an entire mass spectrum is saved at each pixel in an ion image. The analyst often selects only a few species for detailed analysis; the majority of the data are not utilized. Researchers at Sandia National Laboratory (SNL) have developed a powerful multivariate statistical analysis (MVSA) toolkit named AXSIA (Automated eXpert Spectrum Image Analysis) that looks for trends in complete datasets (e.g., analyzes the entire mass spectrum at each pixel). A unique feature of the AXSIA toolkit is the generation of intuitive results (e.g., negative peaks are not allowed in the spectral response). The robust statistical process is able to unambiguously identify all of the spectral features uniquely associated with each distinct component throughout the dataset. General Electric and Sandia used AXSIA to analyze raw data files generated on an Ion Tof IV ToF-SIMS instrument. Here, we will show that the MVSA toolkit identified metallic contaminants within a defect in a polymer sample. These metallic contaminants were not identifiable using standard data analysis protocol. © 2004 Elsevier B.V. All rights reserved.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) by its parallel nature, generates complex and very large datasets quickly and easily. An example of such a large dataset is a spectral image where a complete spectrum is collected for each pixel. Unfortunately, the large size of the data matrix involved makes it difficult to extract the chemical information from the data using traditional techniques. Because time constraints prevent an analysis of every peak, prior knowledge is used to select the most probable and significant peaks for evaluation. However, this approach may lead to a misinterpretation of the system under analysis. Ideally, the complete spectral image would be used to provide a comprehensive, unbiased materials characterization based on full spectral signatures. Automated eXpert spectral image analysis (AXSIA) software developed at Sandia National Laboratories implements a multivariate curve resolution technique that was originally developed for energy dispersive X-ray spectroscopy (EDS) [Microsci. Microanal. 9 (2003) 1]. This paper will demonstrate the application of the method to TOF-SIMS. AXSIA distills complex and very large spectral image datasets into a limited number of physically realizable and easily interpretable chemical components, including both spectra and concentrations. The number of components derived during the analysis represents the minimum number of components needed to completely describe the chemical information in the original dataset. Since full spectral signatures are used to determine each component, an enhanced signal-to-noise is realized. The efficient statistical aggregation of chemical information enables small and unexpected features to be automatically found without user intervention. © 2004 Elsevier B.V. All rights reserved.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Nanometer scale morphological changes in the passive oxide on aluminum have been tracked as a function of polarization in an aqueous, moderate chloride electrolyte. Nanoscale void formation has been detected and characterized in the passive oxide on both single crystal Al and nanocrystalline Al thin films. Void nucleation occurs at the metal/oxide interface and growth proceeds into the oxide. This void formation process correlates with the faradaic charge density produced due to Al oxidation indicating that the voids result from point defect saturation at the Al/oxide interface. The shape factors for the voids are inconsistent with two leading pit initiation models where stable pitting is argued to result from disruption of the remnant oxide over a void or void-like structures. Several experimental observations and measurements suggest this predominant structural feature is not sufficient alone in determining the stability of the passive oxide toward stable pitting. An experiment is proposed and conducted to clearly establish causality between voids and stable pitting, however, the results are inclusive.